The Experts below are selected from a list of 4434 Experts worldwide ranked by ideXlab platform

Hiroyuki Okano - One of the best experts on this subject based on the ideXlab platform.

  • freight simulation the modal shift transportation planning problem and its fast Steepest Descent Algorithm
    Winter Simulation Conference, 2003
    Co-Authors: Masami Amano, Takayuki Yoshizumi, Hiroyuki Okano
    Abstract:

    The Modal-Shift Transportation Planning Problem (MSTPP) is the problem that finds a feasible schedule for carriers with the minimum total cost when sets of facilities, delivery orders, and carriers are given. In this paper, we propose a fast Steepest Descent Algorithm to solve the MSTPP. Our solution generates a set of candidate routes for each delivery order as a preprocess. Then, it finds a schedule by iteratively updating selections of the candidate routes in Descent directions, while computing a configuration of carrier movements at each iteration by a greedy Algorithm. Intensive numerical study using artificial data modeled from the manufacturing industry in Japan is also presented.

  • Winter Simulation Conference - Freight simulation: the modal-shift transportation planning problem and its fast Steepest Descent Algorithm
    2003
    Co-Authors: Masami Amano, Takayuki Yoshizumi, Hiroyuki Okano
    Abstract:

    The Modal-Shift Transportation Planning Problem (MSTPP) is the problem that finds a feasible schedule for carriers with the minimum total cost when sets of facilities, delivery orders, and carriers are given. In this paper, we propose a fast Steepest Descent Algorithm to solve the MSTPP. Our solution generates a set of candidate routes for each delivery order as a preprocess. Then, it finds a schedule by iteratively updating selections of the candidate routes in Descent directions, while computing a configuration of carrier movements at each iteration by a greedy Algorithm. Intensive numerical study using artificial data modeled from the manufacturing industry in Japan is also presented.

Ching-feng Wen - One of the best experts on this subject based on the ideXlab platform.

  • Steepest-Descent Approach to Triple Hierarchical Constrained Optimization Problems
    Abstract and Applied Analysis, 2014
    Co-Authors: Lu-chuan Ceng, Cheng-wen Liao, Chin-tzong Pang, Ching-feng Wen
    Abstract:

    We introduce and analyze a hybrid Steepest-Descent Algorithm by combining Korpelevich’s extragradient method, the Steepest-Descent method, and the averaged mapping approach to the gradient-projection Algorithm. It is proven that under appropriate assumptions, the proposed Algorithm converges strongly to the unique solution of a triple hierarchical constrained optimization problem (THCOP) over the common fixed point set of finitely many nonexpansive mappings, with constraints of finitely many generalized mixed equilibrium problems (GMEPs), finitely many variational inclusions, and a convex minimization problem (CMP) in a real Hilbert space.

Lu-chuan Ceng - One of the best experts on this subject based on the ideXlab platform.

D.s. Rhode - One of the best experts on this subject based on the ideXlab platform.

  • Adaptive control of an arc welding process
    IEEE Control Systems Magazine, 1993
    Co-Authors: D.e. Henderson, J. L. Schiano, Petar V. Kokotović, D.s. Rhode
    Abstract:

    A pseudogradient adaptive Algorithm is successfully applied to self-tune a proportional-integral (PI) puddle-width controller for consumable-electrode gas metal arc welding. The gradient of the output with respect to the controller parameters is approximated and used to form a Steepest-Descent Algorithm to minimize the squared output error. Experimental data confirming the Algorithm performance are presented.

  • Adaptive Control of an Arc Welding Process
    1991 American Control Conference, 1991
    Co-Authors: D.e. Henderson, J. L. Schiano, Petar V. Kokotović, D.s. Rhode
    Abstract:

    This paper reports on the successful application of a pseudogradient adaptive Algorithm for self-tuning a PI puddle width controller for consumable-electrode gas metal arc welding. The gradient of the output with respect to the controller parameters is approximated and used to form a Steepest Descent Algorithm to minimize the squared output error. Experimental data confirming the Algorithm performance is presented.

Masami Amano - One of the best experts on this subject based on the ideXlab platform.

  • freight simulation the modal shift transportation planning problem and its fast Steepest Descent Algorithm
    Winter Simulation Conference, 2003
    Co-Authors: Masami Amano, Takayuki Yoshizumi, Hiroyuki Okano
    Abstract:

    The Modal-Shift Transportation Planning Problem (MSTPP) is the problem that finds a feasible schedule for carriers with the minimum total cost when sets of facilities, delivery orders, and carriers are given. In this paper, we propose a fast Steepest Descent Algorithm to solve the MSTPP. Our solution generates a set of candidate routes for each delivery order as a preprocess. Then, it finds a schedule by iteratively updating selections of the candidate routes in Descent directions, while computing a configuration of carrier movements at each iteration by a greedy Algorithm. Intensive numerical study using artificial data modeled from the manufacturing industry in Japan is also presented.

  • Winter Simulation Conference - Freight simulation: the modal-shift transportation planning problem and its fast Steepest Descent Algorithm
    2003
    Co-Authors: Masami Amano, Takayuki Yoshizumi, Hiroyuki Okano
    Abstract:

    The Modal-Shift Transportation Planning Problem (MSTPP) is the problem that finds a feasible schedule for carriers with the minimum total cost when sets of facilities, delivery orders, and carriers are given. In this paper, we propose a fast Steepest Descent Algorithm to solve the MSTPP. Our solution generates a set of candidate routes for each delivery order as a preprocess. Then, it finds a schedule by iteratively updating selections of the candidate routes in Descent directions, while computing a configuration of carrier movements at each iteration by a greedy Algorithm. Intensive numerical study using artificial data modeled from the manufacturing industry in Japan is also presented.